In the grain industry, the need to qualify, classify and grade seeds using an objective system has long been desired. Presently, these tasks are accomplished by manual, visual inspection and assessment. Inspection of seeds is a task which requires extensive specialized training of human resources. However, seeds may not be graded the same way visually by different individuals, and such visual tasks are time-consuming and tedious, leading to inaccuracies in inspection due to human fatigue. Inspection of seeds can involve assessment of more than one parameter, data which may be too excessive to be obtained efficiently by a human inspector. Since the current manual, visual inspection is labour intensive and highly prone to subjective human error and bias, there is thus a need for a system which accomplishes the qualifying, classification and grading of a sample of seeds objectively and efficiently.
Image analysis relates to a systematic operation or series of operations performed on data representative of an observed image with the aim of measuring a characteristic of the image, detecting variations and structure in the image, or transforming the image in a way that facilitates its interpretation. Computer based image analysis systems are commonly applied to animal, plant, food and hardware inspection. Such systems are able to transform an image to improve its visual quality prior to recognition, and measuring significant characteristics of the image which are representative of the scanned object of interest.
Image analysis systems of the prior art pertaining to grains or seeds appear not to examine the presence/absence of disease, focusing instead upon methods and devices to determine parameters such as size, shape, area, and broken/whole. For example, U.S. Pat. No. 5,917,927 to Satake et al. discloses an apparatus and method for inspection of rice and other grains to determine the content of broken rice grains.
Thus, a method and apparatus which permit an extensive variety of analyses related to classification, disease, environmental situations, and handling of seeds or grains would be advantageous. With respect to classification of seeds, both class and the specific variety within a class is important information. Such detail of classification appears not to be capably provided by the prior art, yet such level of detail is desirable in the grain industry.
Image analysis systems of the prior art suffer disadvantages associated with emphasizing alignment of grains on grooved trays or belts as an essential step. For example, the apparatus of U.S. Pat. No. 5,917,927 to Satake et al. requires alignment of grains side-by-side lengthwise in a grooved tray. The apparatus of U.S. Pat. No. 5,898,792 to Oste et al. includes a conveyor belt which transports kernels to a second belt where a scraper spreads them in one layer to be oriented longitudinally in grooves on the belt. In U.S. Pat. No. 4,975,863 to Sistler et al., the apparatus involves a vacuum source to position kernels for imaging. These alignment features limit the speed and sample sizes that can be accommodated by the prior art systems. A method and apparatus is needed with no necessity for orientation of seeds or other objects required.